Image denoising method and device, storage medium and electronic device

Abstract

Embodiments of the present disclosure provide an image denoising method and device, a storage medium and an electronic device, wherein the method comprises: obtaining a to-be-denoised pixel point in a to-be-processed image; obtaining a plurality of pixel bands in the to-be-processed image according to the to-be-denoised pixel point, each pixel band comprising a plurality of continuous pixel points with the to-be-denoised pixel point as a center position; for each pixel band in a direction, obtaining a correction parameter of each associated pixel point in the pixel band to the to-be-denoised pixel point, the associated pixel point being a pixel point in the pixel band other than the to-be-denoised pixel point, the correction parameter being used to represent noise included in the to-be-denoised pixel point; and adjusting a pixel value of the to-be-denoised pixel point based on the correction parameter of each associated pixel point in each pixel band in each direction to the to-be-denoised pixel point, to obtain a denoised pixel value. The image denoising method provided by the embodiments of the present disclosure can suppress noise while retaining texture details of an image to a certain extent.

Description

Technical Field

[0001] This disclosure relates to the field of image processing technology, and in particular to an image noise reduction method and apparatus, storage medium and electronic device. Background Technology

[0002] Mobile phone photography is extremely important in daily life, but the raw images captured by the phone's sensors contain a lot of noise. Therefore, special noise reduction methods are needed to reduce image noise.

[0003] In image denoising, the most commonly used method is mean filtering, which estimates the noise-free gray value of a pixel by using the average gray value of its neighborhood in the noisy image. Specifically, a neighborhood S is taken of the pixel (x, y) to be processed. Neighborhood S consists of several pixels that are its nearest neighbors. The size and shape of neighborhood S are determined based on the characteristics of the image, typically taking the form of a square, rectangle, or cross. Then, the mean value of all pixels within this neighborhood is calculated, and this mean value is assigned to the current pixel (x, y) as the gray value of that pixel in the processed image.

[0004] Mean filtering can suppress noise, but because it applies mean filtering to every pixel in the image, it also destroys the texture details of the image while removing noise, making the image blurry. Summary of the Invention

[0005] In view of the above, embodiments of this disclosure provide an image noise reduction method and apparatus, a storage medium and an electronic device.

[0006] According to a first aspect of this disclosure, an image denoising method is proposed, the method comprising:

[0007] Obtain the pixels in the image to be processed that need noise reduction;

[0008] Based on the pixel to be denoised, multiple pixel bands in the image to be processed are obtained. Each pixel band includes multiple consecutive pixels centered on the pixel to be denoised. Each pixel band includes the same number of pixels, and the multiple pixel bands are located in multiple directions centered on the pixel to be denoised.

[0009] For each pixel band in each direction, obtain the correction parameters of each associated pixel in the pixel band for the pixel to be denoised. The associated pixel is the pixel in the pixel band other than the pixel to be denoised. The correction parameters are used to characterize the noise included in the pixel to be denoised.

[0010] Based on the correction parameters of each associated pixel in the pixel band in each direction, the pixel value of the pixel to be denoised is adjusted to obtain the denoised pixel value.

[0011] In conjunction with any embodiment provided in this disclosure, the step of obtaining the correction parameters of each associated pixel point in the pixel band for the pixel point to be denoised for each direction includes:

[0012] Based on the pixel values ​​of each pixel in the plurality of pixel bands, the peripheral noise-to-signal ratio of the pixel to be denoised is determined;

[0013] The noise reduction intensity parameter of the pixel to be denoised is determined based on the peripheral noise-to-signal ratio of the pixel to be denoised and the brightness information of the pixel to be denoised.

[0014] For any associated pixel in the pixel band, the estimated noise corresponding to the associated pixel is obtained based on the pixel value of the pixel to be denoised, the denoising strength parameter of the pixel to be denoised, and the pixel value of the associated pixel.

[0015] Based on the estimated noise corresponding to each associated pixel in the pixel band and the weight parameters corresponding to each associated pixel, the correction parameters of the associated pixel for the pixel to be denoised are determined.

[0016] In conjunction with any embodiment provided in this disclosure, the step of obtaining the estimated noise corresponding to any associated pixel on the pixel band, based on the pixel value of the pixel to be denoised, the denoising intensity parameter of the pixel to be denoised, and the pixel value of the associated pixel, includes:

[0017] The noise threshold is determined based on the noise reduction intensity parameters of the pixel to be denoised, the pixel value of the pixel to be denoised, and the pixel value of the associated pixel.

[0018] In response to the noise threshold being greater than the pixel difference, the pixel difference is used as the estimated noise, where the pixel difference is the difference between the pixel value of the associated pixel and the pixel value of the pixel to be denoised;

[0019] Alternatively, in response to the noise threshold being less than the pixel difference, the noise threshold is used as the estimated noise.

[0020] In conjunction with any embodiment provided in this disclosure, determining the noise threshold based on the denoising intensity parameter of the pixel to be denoised, the pixel value of the pixel to be denoised, and the pixel value of the associated pixel includes:

[0021] Based on the pixel values ​​of the pixel to be denoised and the pixel values ​​of the associated pixel, the noise threshold parameter is obtained;

[0022] Based on the denoising intensity parameter of the pixel to be denoised and the noise threshold parameter, the noise threshold is determined; wherein, the denoising intensity parameter of the pixel to be denoised satisfies the following condition: the greater the brightness of the pixel to be denoised, the smaller the denoising intensity parameter of the pixel to be denoised.

[0023] In conjunction with any embodiment provided in this disclosure, determining the peripheral noise-to-signal ratio of the pixel to be denoised based on the pixel values ​​of each pixel in the plurality of pixel bands includes:

[0024] For each pixel band in a direction, determine the average pixel value of each pixel in that pixel band.

[0025] The pixel change parameter corresponding to the pixel band in that direction is determined based on the difference between the average pixel value of each pixel in the pixel band in that direction and the pixel value of each pixel.

[0026] The peripheral signal-to-noise ratio of the pixel to be denoised is determined based on the pixel change parameters corresponding to the pixel bands in each direction.

[0027] In conjunction with any embodiment provided in this disclosure, adjusting the pixel value of the pixel to be denoised based on the correction parameters of each associated pixel in the pixel band in each direction to obtain the denoised pixel value includes:

[0028] For each pixel band in a direction, the pixel change parameter corresponding to that pixel band is determined based on the pixel value of each pixel in that pixel band.

[0029] The signal strength of the pixel to be denoised is determined based on the pixel change parameters corresponding to the pixel bands in each direction.

[0030] Based on the signal strength of the pixel to be denoised and the pixel change degree parameter corresponding to the pixel band in that direction, the normalization parameter corresponding to the pixel band in that direction is obtained; wherein, the normalization parameter satisfies the following: when the pixel change degree parameter is equal to the signal strength, the normalization parameter is 0;

[0031] Based on the correction parameters of each associated pixel in the pixel band in each direction for the pixel to be denoised, and the normalization parameters corresponding to the pixel band in the corresponding direction, the pixel value of the pixel to be denoised is adjusted to obtain the denoised pixel value; wherein, when the normalization parameter is 0, the correction parameters of each associated pixel in the pixel band in the corresponding direction for the pixel to be denoised do not participate in the adjustment of the pixel value of the pixel to be denoised.

[0032] According to a second aspect of this disclosure, an image noise reduction apparatus is provided, the apparatus comprising:

[0033] The module for acquiring pixels to be denoised is used to acquire the pixels to be denoised in the image to be processed;

[0034] The pixel band acquisition module is used to acquire multiple pixel bands in the image to be processed based on the pixel to be denoised. Each pixel band includes multiple consecutive pixels centered on the pixel to be denoised. The number of pixels included in each pixel band is the same, and the multiple pixel bands are respectively located in multiple directions centered on the pixel to be denoised.

[0035] The correction parameter acquisition module is used to acquire, for each direction of the pixel band, the correction parameters of each associated pixel point in the pixel band to the pixel point to be denoised, wherein the associated pixel points are the pixel points in the pixel band other than the pixel point to be denoised, and the correction parameters are used to characterize the noise included in the pixel point to be denoised.

[0036] The pixel value adjustment module is used to adjust the pixel value of the pixel to be denoised based on the correction parameters of each associated pixel in the pixel band in each direction, so as to obtain the denoised pixel value.

[0037] In conjunction with any embodiment provided in this disclosure, the correction parameter acquisition module, when acquiring the correction parameters of each associated pixel in the pixel band for the pixel to be denoised for each direction, includes:

[0038] Based on the pixel values ​​of each pixel in the plurality of pixel bands, the peripheral noise-to-signal ratio of the pixel to be denoised is determined;

[0039] The noise reduction intensity parameter of the pixel to be denoised is determined based on the peripheral noise-to-signal ratio of the pixel to be denoised and the brightness information of the pixel to be denoised.

[0040] For any associated pixel in the pixel band, the estimated noise corresponding to the associated pixel is obtained based on the pixel value of the pixel to be denoised, the denoising strength parameter of the pixel to be denoised, and the pixel value of the associated pixel.

[0041] Based on the estimated noise corresponding to each associated pixel in the pixel band and the weight parameters corresponding to each associated pixel, the correction parameters of the associated pixel for the pixel to be denoised are determined.

[0042] In conjunction with any embodiment provided in this disclosure, the correction parameter acquisition module, when used to acquire the estimated noise corresponding to any associated pixel on the pixel band based on the pixel value of the pixel to be denoised, the denoising intensity parameter of the pixel to be denoised, and the pixel value of the associated pixel, includes:

[0043] The noise threshold is determined based on the noise reduction intensity parameters of the pixel to be denoised, the pixel value of the pixel to be denoised, and the pixel value of the associated pixel.

[0044] In response to the noise threshold being greater than the pixel difference, the pixel difference is used as the estimated noise, where the pixel difference is the difference between the pixel value of the associated pixel and the pixel value of the pixel to be denoised;

[0045] Alternatively, in response to the noise threshold being less than the pixel difference, the noise threshold is used as the estimated noise.

[0046] In conjunction with any embodiment provided in this disclosure, the correction parameter acquisition module, when determining a noise threshold based on the denoising intensity parameter of the pixel to be denoised, the pixel value of the pixel to be denoised, and the pixel value of the associated pixel, includes:

[0047] Based on the pixel values ​​of the pixel to be denoised and the pixel values ​​of the associated pixel, the noise threshold parameter is obtained;

[0048] Based on the denoising intensity parameter of the pixel to be denoised and the noise threshold parameter, the noise threshold is determined; wherein, the denoising intensity parameter of the pixel to be denoised satisfies the following condition: the greater the brightness of the pixel to be denoised, the smaller the denoising intensity parameter of the pixel to be denoised.

[0049] In conjunction with any embodiment provided in this disclosure, the correction parameter acquisition module, when determining the peripheral noise-to-signal ratio of the pixel to be denoised based on the pixel values ​​of each pixel in the plurality of pixel bands, includes:

[0050] For each pixel band in a direction, determine the average pixel value of each pixel in that pixel band.

[0051] The pixel change parameter corresponding to the pixel band in that direction is determined based on the difference between the average pixel value of each pixel in the pixel band in that direction and the pixel value of each pixel.

[0052] The peripheral signal-to-noise ratio of the pixel to be denoised is determined based on the pixel change parameters corresponding to the pixel bands in each direction.

[0053] In conjunction with any embodiment provided in this disclosure, the pixel value adjustment module, when adjusting the pixel value of the pixel to be denoised based on the correction parameters of each associated pixel in the pixel band in each direction to obtain the denoised pixel value, includes:

[0054] For each pixel band in a direction, the pixel change parameter corresponding to that pixel band is determined based on the pixel value of each pixel in that pixel band.

[0055] The signal strength of the pixel to be denoised is determined based on the pixel change parameters corresponding to the pixel bands in each direction.

[0056] Based on the signal strength of the pixel to be denoised and the pixel change degree parameter corresponding to the pixel band in that direction, the normalization parameter corresponding to the pixel band in that direction is obtained; wherein, the normalization parameter satisfies the following: when the pixel change degree parameter is equal to the signal strength, the normalization parameter is 0;

[0057] Based on the correction parameters of each associated pixel in the pixel band in each direction for the pixel to be denoised, and the normalization parameters corresponding to the pixel band in the corresponding direction, the pixel value of the pixel to be denoised is adjusted to obtain the denoised pixel value; wherein, when the normalization parameter is 0, the correction parameters of each associated pixel in the pixel band in the corresponding direction for the pixel to be denoised do not participate in the adjustment of the pixel value of the pixel to be denoised.

[0058] According to a third aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements an image noise reduction method according to any embodiment of this disclosure.

[0059] According to a fourth aspect of this disclosure, an electronic device is provided, comprising:

[0060] processor;

[0061] Memory used to store processor-executable instructions;

[0062] The processor is configured to perform an image denoising method according to any embodiment of the present disclosure.

[0063] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0064] The image denoising method provided in this disclosure involves, for each pixel in the image to be denoised, obtaining multiple pixel bands centered on the pixel to be denoised. For each pixel band in each direction, obtaining correction parameters of each associated pixel in the pixel band relative to the pixel to be denoised, and performing denoising processing on the pixel to be denoised based on the correction parameters of each associated pixel in the pixel band in each direction. This suppresses noise while preserving some of the texture details of the image.

[0065] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0066] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0067] Figure 1 This is a flowchart illustrating an image noise reduction method according to an exemplary embodiment of the present disclosure;

[0068] Figure 2 This disclosure is a schematic diagram of a pixel band distribution according to an exemplary embodiment;

[0069] Figure 3 This disclosure is a schematic diagram illustrating the pixel distribution in a pixel band according to an exemplary embodiment;

[0070] Figure 4 This disclosure illustrates a correspondence between weight parameters and relative positions according to an exemplary embodiment.

[0071] Figure 5 This is a flowchart illustrating yet another image denoising method according to an exemplary embodiment of the present disclosure;

[0072] Figure 6 This is a schematic diagram of the structure of an image noise reduction apparatus according to an exemplary embodiment of the present disclosure;

[0073] Figure 7 This is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0074] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0075] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The singular forms “a,” “the,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0076] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this disclosure, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0077] The image noise reduction method of this disclosure will now be described in detail with reference to the accompanying drawings.

[0078] Figure 1 This is a flowchart illustrating an image noise reduction method according to an exemplary embodiment of the present disclosure.

[0079] like Figure 1 As shown, the exemplary embodiment method may include the following steps:

[0080] Step S101: Obtain the pixels to be denoised in the image to be processed.

[0081] The image to be processed is an image that has not undergone noise reduction processing. The image to be processed can be a raw image captured by a mobile phone sensor or an image downloaded from the Internet; this disclosure does not impose specific limitations on it.

[0082] For the acquired image to be processed, each pixel in the image to be processed is processed by the image denoising method shown in the embodiments of this disclosure. Pixels that have not yet been processed by the image denoising method are the pixels to be denoised. This disclosure describes the image denoising method of the embodiments of this disclosure in detail by processing one pixel to be denoised.

[0083] Step S102: Based on the pixels to be denoised, obtain multiple pixel bands in the image to be processed.

[0084] Each pixel band includes multiple consecutive pixels centered on the pixel to be denoised. Each pixel band contains the same number of pixels, and the multiple pixel bands are located in multiple directions centered on the pixel to be denoised.

[0085] In an optional example, based on the pixels to be denoised, the multiple pixel bands in the image to be processed can be obtained as follows: Figure 2 As shown. In this example, the pixel P to be denoised is used. 10 Centered on the image, it is divided into eight equal directions within a 360-degree radius, with three consecutive pixels extending along each direction. For example... Figure 2 As shown, according to the pixel P to be denoised 10 Eight pixel bands were obtained from the image to be processed, such as pixel band 21 and pixel band 22.

[0086] In an optional example, the pixel to be denoised can be divided into three or four equal directions within a 180-degree range, with three or four pixels extending along each direction. The angle range of the division directions and the number of pixels extending along each direction can be determined by those skilled in the art based on the actual situation; this disclosure does not impose specific limitations on these aspects.

[0087] Step S103: For each pixel band in each direction, obtain the correction parameters of each associated pixel point in the pixel band for the pixel point to be denoised.

[0088] Wherein, the associated pixels are the pixels in the pixel band other than the pixel to be denoised. Figure 3 Taking the pixel band shown as an example, pixel point P 10 Let P be the pixel to be denoised. 11 P 12 P 13 P 14 P 15 P 16 These are the associated pixels in the pixel band. The correction parameter is used to characterize the noise included in the pixels to be denoised.

[0089] In an optional example, the peripheral noise-to-signal ratio (PSNR) of the pixel to be denoised can be determined first based on the pixel values ​​of each pixel in the plurality of pixel bands. The PSNR of the pixel to be denoised is the ratio of the peripheral noise of the pixel to the peripheral signal intensity of the pixel.

[0090] In one example, the method for determining the peripheral noise-to-signal ratio of the pixel to be denoised is as follows:

[0091] For each pixel band in each direction, first determine the average pixel value of each pixel in that direction. Then, based on the difference between the average pixel value and the individual pixel values ​​in that direction, determine the pixel variation parameter corresponding to that direction. Furthermore, the peripheral signal-to-noise ratio of the pixel to be denoised can be determined based on the pixel variation parameter corresponding to each direction.

[0092] by Figure 3 Taking the pixel band along the indicated direction as an example, let the current direction be D1. Then the average pixel value of each pixel in the pixel band along the D1 direction is:

[0093]

[0094] in, p is the average pixel value of all pixels in the pixel band along the D1 direction. 1j This represents the pixel value of each pixel in the pixel band along the D1 direction.

[0095] Based on this average value, the pixel change parameter corresponding to the pixel band in the D1 direction is determined according to the following formula (2):

[0096]

[0097] Wherein, div1 represents the pixel change degree parameter corresponding to the pixel band in the D1 direction, which can also be called the difference corresponding to the pixel band in the D1 direction.

[0098] The difference reflects the degree of drastic change in pixel values ​​within a pixel band, and the degree of drastic change in pixel values ​​represents signal strength. Specifically, the larger the difference, the more drastic the pixel value change in that direction, and the greater the signal strength in that direction.

[0099] Therefore, for Figure 2 The pixel P to be denoised in 10 The above method can be used to obtain Figure 2 The pixel bands in each of the eight directions shown correspond to the difference divs. i Therefore, the surrounding signal strength div of the pixel to be denoised can be obtained. max ,Right now Figure 2 The maximum value of the difference corresponding to the pixel band in each of the eight directions shown:

[0100] div max =max i＝1，…，8(div i (3)

[0101] and surrounding noise div sum ,Right now Figure 2 The sum of the differences corresponding to the pixel bands in each of the eight directions shown:

[0102]

[0103] Among them, div sum Essentially, it is the sum of the surrounding signal intensity and the surrounding noise of the pixel to be denoised. However, in the image denoising method of this embodiment, it can be approximated as the sum of the surrounding noise of the pixel to be denoised.

[0104] Based on the above, in this example, the reaction can be performed using the following formula (5). Figure 2 The pixel P to be denoised is shown. 10 Surrounding noise signal ratio div ratio :

[0105]

[0106] In this example, the area is divided into eight equal parts within a 360-degree radius, centered on the pixel to be denoted. Therefore, the denominator is 8 multiplied by div. max When the pixel to be denoised is divided into 9 equal directions within a 360-degree range, the corresponding denominator is 9 multiplied by div. max .

[0107] In practical applications, it's observed that in well-lit areas, image noise is lower, thus requiring less denoising effort and preserving more texture details. Conversely, in dimly lit areas, image noise is higher, necessitating more denoising effort to achieve better results. Therefore, the denoising effort required for the image to be processed should be related to the image's brightness.

[0108] Therefore, after determining the peripheral noise-to-signal ratio of the pixel to be denoised, the denoising strength parameter of the pixel to be denoised can be further determined based on the peripheral noise-to-signal ratio and the brightness information of the pixel to be denoised.

[0109] The brightness information l of the pixel to be denoised can be determined by a brightness map L, which shows the correspondence between the brightness of the pixel to be denoised and its brightness information l. This disclosure does not limit the specific form of the brightness map L, as long as it satisfies the condition that the greater the brightness of the pixel to be denoised, the smaller the value of the corresponding brightness information l.

[0110] In this example, the denoising strength parameter of the pixel to be denoised can be obtained by the following formula:

[0111]

[0112] Where v0 is the denoising intensity parameter of the pixel to be denoised, and l is the brightness information corresponding to the pixel to be denoised. In this example, the pixel to be denoised is centered on the 8 equal directions within a 360-degree range; therefore, the denominator is 8 multiplied by div. max The numerator is 4 times div max In another example, when the pixel to be denoised is divided into 10 equal directions within a 360-degree range, the denominator should be 10 multiplied by div. max The numerator is 5 multiplied by div max .

[0113] As can be seen from formula (6), the magnitude of the denoising strength parameter v0 of the pixel to be denoised is controlled by two parts: one part is directly affected by the brightness information l of the pixel to be denoised, and the other part is affected by the surrounding noise-to-signal ratio div of the pixel to be denoised. ratio The combined constraints of brightness information l.

[0114] In practical applications, the higher the brightness of the pixel to be denoised, the smaller the corresponding brightness information l, resulting in a smaller denoising strength parameter v0 for that pixel. Conversely, the higher the brightness of the pixel, the lower the surrounding noise and the lower the surrounding noise-to-signal ratio (SNR), leading to a smaller denoising strength parameter v0 and thus a weaker denoising effort. Similarly, the lower the brightness of the pixel, the higher the corresponding brightness information l, resulting in a larger denoising strength parameter v0. Finally, the lower the brightness of the pixel, the higher the surrounding noise and the higher the surrounding noise-to-signal ratio (SNR), leading to a larger denoising strength parameter v0 and thus a stronger denoising effort.

[0115] In this example, the denoising intensity parameter of the pixel to be denoised is determined by the brightness of the pixel to be denoised. This avoids the problem that when using a uniform denoising intensity to denoise images in areas with different light intensities, texture loss occurs in areas with high light intensity, while denoising is ineffective in areas with low light intensity.

[0116] After obtaining the denoising intensity parameter of the pixel to be denoised, for any associated pixel in the pixel band, the estimated noise corresponding to the associated pixel can be obtained based on the pixel value of the pixel to be denoised, the denoising intensity parameter of the pixel to be denoised, and the pixel value of the associated pixel.

[0117] In one optional example, a noise threshold can be determined based on the denoising intensity parameter of the pixel to be denoised, the pixel value of the pixel to be denoised, and the pixel value of the associated pixel. For example, when the noise threshold is greater than the pixel difference, the pixel difference is used as the estimated noise. Alternatively, when the noise threshold is less than the pixel difference, the noise threshold is used as the estimated noise.

[0118] The pixel difference is the difference between the pixel value of the associated pixel and the pixel value of the pixel to be denoised.

[0119] In one optional example, the confirmation process for the noise threshold can be as follows:

[0120] Based on the pixel value of the pixel to be denoised and the pixel value of the associated pixel, a noise threshold parameter is obtained, and then the noise threshold is determined based on the denoising intensity parameter of the pixel to be denoised and the noise threshold parameter.

[0121] by Figure 3 Related pixel p 11 And the pixel to be denoised p 10 Let's take an example to illustrate.

[0122] After obtaining the associated pixel point p 11 and the pixel point p to be denoised 10 After obtaining the pixel values, the noise threshold parameter w can be determined according to the following formula. 11 :

[0123]

[0124] Furthermore, based on the pixel point p to be denoised 10 The denoising strength parameter v0 and the noise threshold parameter w 11 Determine the associated pixel point p 11 The corresponding noise threshold z 11 :

[0125]

[0126] The calculation process for the noise threshold corresponding to each other associated pixel is the same as that for the associated pixel p described above. 11 The corresponding noise threshold z 11The calculation process is similar and will not be repeated here.

[0127] In determining the noise threshold z 11 Then, further, the pixel difference, i.e., the associated pixel p, can be calculated. 11 The pixel value and the pixel point p to be denoised 10 The difference in pixel values. When the noise threshold is greater than the pixel difference, the pixel difference is used as the estimated noise. When the noise threshold is less than the pixel difference, the noise threshold is used as the estimated noise. Specifically, the estimated noise Q(p) can be represented by the following formula (9). 10 p 11 v0):

[0128]

[0129] Generalizing to the general case, the noise Q(p) is estimated. 10 p ij v0):

[0130]

[0131] Where, p ij Indicates as Figure 2 In each pixel band shown, except for the pixel point p to be denoised 10 The pixel value of any associated pixel other than i, where i is used to identify pixel bands in different directions, and j is used to identify different associated pixels in the pixel band in direction i. It can be seen that formula (10) performs a max operation before performing the min operation, because:

[0132] In practical applications, when When p is less than 0 ij -p 10 and Compared to the former, it has a stronger filtering effect, i.e., p 11 -p 10 The noise reduction effect is greater. Since v0 is greater than 0, therefore, when When the value is less than 0, it is usually due to the pixel p to be denoised. 10 The pixel value is much larger than its associated pixel p. ij In this case, the pixel value to be denoised can be considered a noise point, requiring strong filtering, i.e., it needs to use p ij -p 10 As the estimated noise Q(p) 10 p ij (v0). But when When p is less than 0 ij -p 10It is also less than 0. To ensure that the estimated noise is taken to p in this case. ij -p 10 Therefore, before performing the min operation, the max operation is performed first, so that the result of the max operation is 0, and the result of the min operation is p. ij -p 10 That is, the max operation in formula (10) is for when When less than 0, estimate the noise Q(p) 10 p ij The value of v0 is p. ij -p 10 .

[0133] Furthermore, in areas with high light intensity and good signal-to-noise ratio, there will be areas with lower brightness information (l) and lower noise-to-signal ratio (div). ratio The smaller value results in a smaller noise reduction parameter v0, which further makes the noise reduction strength parameter v0 smaller. This means that in areas with high light intensity and good signal-to-noise ratio, the filtering effect will be weaker, thus preserving more details.

[0134] After obtaining the estimated noise corresponding to the associated pixel, the correction parameters of the associated pixel for the pixel to be denoised can be determined based on the estimated noise corresponding to each associated pixel in the pixel band and the weight parameters corresponding to each associated pixel.

[0135] In an optional example, the weight parameter corresponding to the associated pixel is related to the relative position of the associated pixel and the pixel to be denoised. For example, the relationship between the weight parameter corresponding to the associated pixel and the relative position of the associated pixel and the pixel to be denoised can be as follows: Figure 4 As shown, the closer the associated pixel is to the pixel to be denoised, the larger the weight parameter; the farther the distance, the smaller the weight parameter.

[0136] Based on the above, with the associated pixel point p 11 For example, the correction parameters for the pixel to be denoised are as follows:

[0137]

[0138] Depend on Figure 4 As can be seen, in this example, the sum of the weights of the pixel bands in each direction is 12, therefore, the denominator in formula (10) is 12. When the sum of the weight parameters of the pixel bands in each direction is 16, the denominator in formula (10) is 16.

[0139] Because formula (6) uses div when calculating the denoising strength parameter v0 of the pixel to be denoised. max and div sum, and the div max and div sum This can also determine the signal-to-noise ratio (SNR) of the pixel to be denoised, thus obtaining the SNR of the pixel to be denoised. In formula (11), the associated pixel p is calculated. 11 For the pixel point p to be denoised 10 The denoising intensity parameter v0 was used when the correction parameters were calculated. Therefore, in this example, the correction parameters of the associated pixel to the pixel to be denoised are related not only to the relative position of the associated pixel and the pixel to be denoised, but also to the signal-to-noise ratio of the pixel to be denoised.

[0140] Step S104: Based on the correction parameters of the pixel to be denoised for each associated pixel in the pixel band in each direction, the pixel value of the pixel to be denoised is adjusted to obtain the denoised pixel value. The specific formula can be expressed as follows:

[0141]

[0142] Wherein, p1′0 is the pixel point p to be denoised. 10 The denoised pixel values, when i=1, It represents the weighted average of the correction parameters of all pixels in a pixel band to the pixel to be denoised. This represents the weighted average of the correction parameters of all pixels in the pixel bands in the eight directions for the pixel band to be denoised.

[0143] The image denoising method provided in this disclosure, for each pixel in the image to be denoised, obtains multiple pixel bands centered on the pixel to be denoised. For each pixel band in each direction, it obtains correction parameters of each associated pixel in the pixel band relative to the pixel to be denoised, and performs denoising processing on the pixel to be denoised based on these correction parameters. This suppresses noise while preserving some of the image's texture details. Furthermore, since the image denoising method in this example uses basic addition, subtraction, multiplication, and division operations, and the range of the selected associated pixels is relatively small, the computational load is low and the computation speed is fast.

[0144] Figure 5This is a flowchart illustrating another image quality adjustment method according to an exemplary embodiment. In the description of this embodiment, steps identical to those in any of the foregoing embodiments will be briefly described and will not be detailed further; however, specific details can be found in any of the foregoing embodiments. In the foregoing embodiments, noise reduction is performed on the pixel to be denoised based on the pixel values ​​of pixels in pixel bands in multiple directions surrounding the pixel to be denoised. In this example, edge information of the image can be further preserved based on the above. For example... Figure 5 As shown, the exemplary embodiment method may include the following steps:

[0145] Step S501: Obtain the pixels to be denoised in the image to be processed.

[0146] Step S502: Based on the pixels to be denoised, obtain multiple pixel bands in the image to be processed.

[0147] Step S503: For each pixel band in each direction, obtain the correction parameters of each associated pixel point in the pixel band for the pixel point to be denoised.

[0148] Step S504: For each pixel band in a direction, determine the pixel change degree parameter corresponding to the pixel band in that direction based on the pixel value of each pixel in the pixel band in that direction.

[0149] Step S505: Determine the signal strength of the pixel to be denoised based on the pixel change parameters corresponding to the pixel bands in each direction.

[0150] Step S506: Based on the signal strength of the pixel to be denoised and the pixel change parameter corresponding to the pixel band in that direction, obtain the normalization parameter corresponding to the pixel band in that direction.

[0151] The normalization parameter satisfies the following condition: when the pixel change parameter is equal to the signal strength, the normalization parameter is 0.

[0152] In an optional example, the normalized parameter can be obtained according to formula (13):

[0153]

[0154] The number 8 in the denominator is determined by the number of pixel bands divided. When the number of pixel bands divided with the pixel to be denoised as the center is 6, the number in the denominator is 6. From formula (13), it can be seen that when the pixel change parameter div... i With the signal strength div max When they are equal, the normalization parameter is 0.

[0155] Step S507: Based on the correction parameters of each associated pixel in the pixel band in each direction for the pixel to be denoised, and the normalization parameters corresponding to the pixel band in the corresponding direction, the pixel value of the pixel to be denoised is adjusted to obtain the denoised pixel value.

[0156] In an optional example, the pixel values ​​of the pixels to be denoised can be adjusted using the following formula:

[0157]

[0158] To make it easier to understand, the above formula can be transformed into:

[0159]

[0160]

[0161] The meanings of each letter in formulas (14) and (15) have been explained in detail in the aforementioned embodiments and will not be repeated here.

[0162] As can be seen from formula (15), when the normalization parameter is 0, i.e. When the value is 0, the correction parameter of each associated pixel in the pixel band in the direction corresponding to the normalization parameter for the pixel to be denoised is 0, that is, each associated pixel in the pixel band in the direction corresponding to the normalization parameter does not participate in the adjustment of the pixel value of the pixel to be denoised.

[0163] Specifically, when When the value is 0, div max -div i It is 0, that is, div max =div i As can be seen from the foregoing embodiments, the div max The div represents the surrounding signal strength of the pixel to be denoised. i The pixel in the i-direction carries the corresponding pixel change parameter. When div max =div i When i is in the direction of signal strength, the pixels in the pixel band in the i direction do not participate in the adjustment of the pixel value of the pixel to be denoised, thus preserving the edge information of the image.

[0164] The image denoising method of this disclosure performs denoising processing on the pixel to be denoised by the normalization parameters and the correction parameters of each associated pixel in the pixel band in each direction for the pixel to be denoised. This makes it so that when the direction of the pixel band is the signal intensity direction, the pixels in the pixel band do not participate in the adjustment of the pixel value of the pixel to be denoised, which can effectively distinguish noise and texture details in the image, and preserve the texture details in the image to the greatest extent while suppressing noise.

[0165] For the foregoing method embodiments, in order to simplify the description, they are all described as a series of actions. However, those skilled in the art should know that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps may be performed in other orders or simultaneously.

[0166] Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by this disclosure.

[0167] Corresponding to the aforementioned application function implementation method embodiments, this disclosure also provides embodiments of application function implementation apparatus and corresponding terminals.

[0168] Figure 6 This is a schematic diagram of the structure of an image noise reduction apparatus in an exemplary embodiment of this disclosure, as shown below. Figure 6 As shown, the image noise reduction device may include:

[0169] The pixel acquisition module 61 is used to acquire the pixel to be denoised in the image to be processed;

[0170] The pixel band acquisition module 62 is used to acquire multiple pixel bands in the image to be processed based on the pixel to be denoised. Each pixel band includes multiple consecutive pixels centered on the pixel to be denoised. The number of pixels included in each pixel band is the same, and the multiple pixel bands are respectively located in multiple directions centered on the pixel to be denoised.

[0171] The correction parameter acquisition module 63 is used to acquire, for each direction of the pixel band, the correction parameters of each associated pixel point in the pixel band to the pixel point to be denoised, wherein the associated pixel points are the pixel points in the pixel band other than the pixel point to be denoised, and the correction parameters are used to characterize the noise included in the pixel point to be denoised.

[0172] The pixel value adjustment module 64 is used to adjust the pixel value of the pixel to be denoised based on the correction parameters of each associated pixel in the pixel band in each direction, so as to obtain the denoised pixel value.

[0173] Optionally, the correction parameter acquisition module 63, when acquiring the correction parameters of each associated pixel in the pixel band for the pixel to be denoised for each direction, includes:

[0174] Based on the pixel values ​​of each pixel in the plurality of pixel bands, the peripheral noise-to-signal ratio of the pixel to be denoised is determined;

[0175] The noise reduction intensity parameter of the pixel to be denoised is determined based on the peripheral noise-to-signal ratio of the pixel to be denoised and the brightness information of the pixel to be denoised.

[0176] For any associated pixel in the pixel band, the estimated noise corresponding to the associated pixel is obtained based on the pixel value of the pixel to be denoised, the denoising strength parameter of the pixel to be denoised, and the pixel value of the associated pixel.

[0177] Based on the estimated noise corresponding to each associated pixel in the pixel band and the weight parameters corresponding to each associated pixel, the correction parameters of the associated pixel for the pixel to be denoised are determined.

[0178] Optionally, the correction parameter acquisition module 63, when acquiring the estimated noise corresponding to any associated pixel on the pixel band based on the pixel value of the pixel to be denoised, the denoising intensity parameter of the pixel to be denoised, and the pixel value of the associated pixel, includes:

[0179] The noise threshold is determined based on the noise reduction intensity parameters of the pixel to be denoised, the pixel value of the pixel to be denoised, and the pixel value of the associated pixel.

[0180] In response to the noise threshold being greater than the pixel difference, the pixel difference is used as the estimated noise, where the pixel difference is the difference between the pixel value of the associated pixel and the pixel value of the pixel to be denoised;

[0181] Alternatively, in response to the noise threshold being less than the pixel difference, the noise threshold is used as the estimated noise.

[0182] Optionally, the correction parameter acquisition module 63, when determining the noise threshold based on the denoising intensity parameter of the pixel to be denoised, the pixel value of the pixel to be denoised, and the pixel value of the associated pixel, includes:

[0183] Based on the pixel values ​​of the pixel to be denoised and the pixel values ​​of the associated pixel, the noise threshold parameter is obtained;

[0184] Based on the denoising intensity parameter of the pixel to be denoised and the noise threshold parameter, the noise threshold is determined; wherein, the denoising intensity parameter of the pixel to be denoised satisfies the following condition: the greater the brightness of the pixel to be denoised, the smaller the denoising intensity parameter of the pixel to be denoised.

[0185] Optionally, the correction parameter acquisition module 63, when determining the peripheral noise-to-signal ratio of the pixel to be denoised based on the pixel values ​​of each pixel in the plurality of pixel bands, includes:

[0186] For each pixel band in a direction, determine the average pixel value of each pixel in that pixel band.

[0187] The pixel change parameter corresponding to the pixel band in that direction is determined based on the difference between the average pixel value of each pixel in the pixel band in that direction and the pixel value of each pixel.

[0188] The peripheral signal-to-noise ratio of the pixel to be denoised is determined based on the pixel change parameters corresponding to the pixel bands in each direction.

[0189] Optionally, the pixel value adjustment module 64, when adjusting the pixel value of the pixel to be denoised based on the correction parameters of each associated pixel in the pixel band in each direction to obtain the denoised pixel value, includes:

[0190] For each pixel band in a direction, the pixel change parameter corresponding to that pixel band is determined based on the pixel value of each pixel in that pixel band.

[0191] The signal strength of the pixel to be denoised is determined based on the pixel change parameters corresponding to the pixel bands in each direction.

[0192] Based on the signal strength of the pixel to be denoised and the pixel change degree parameter corresponding to the pixel band in that direction, the normalization parameter corresponding to the pixel band in that direction is obtained; wherein, the normalization parameter satisfies the following: when the pixel change degree parameter is equal to the signal strength, the normalization parameter is 0;

[0193] Based on the correction parameters of each associated pixel in the pixel band in each direction for the pixel to be denoised, and the normalization parameters corresponding to the pixel band in the corresponding direction, the pixel value of the pixel to be denoised is adjusted to obtain the denoised pixel value; wherein, when the normalization parameter is 0, the correction parameters of each associated pixel in the pixel band in the corresponding direction for the pixel to be denoised do not participate in the adjustment of the pixel value of the pixel to be denoised.

[0194] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0195] Accordingly, this disclosure provides an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to:

[0196] Obtain the pixels in the image to be processed that need noise reduction;

[0197] Based on the pixel to be denoised, multiple pixel bands in the image to be processed are obtained. Each pixel band includes multiple consecutive pixels centered on the pixel to be denoised. Each pixel band includes the same number of pixels, and the multiple pixel bands are located in multiple directions centered on the pixel to be denoised.

[0198] For each pixel band in each direction, obtain the correction parameters of each associated pixel in the pixel band for the pixel to be denoised. The associated pixel is the pixel in the pixel band other than the pixel to be denoised. The correction parameters are used to characterize the noise included in the pixel to be denoised.

[0199] Based on the correction parameters of each associated pixel in the pixel band in each direction, the pixel value of the pixel to be denoised is adjusted to obtain the denoised pixel value.

[0200] Figure 7 This is a schematic diagram illustrating the structure of an electronic device 700 according to an exemplary embodiment. For example, the electronic device 700 can be a user device, specifically a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, wearable device such as smartwatch, smart glasses, smart bracelet, smart running shoes, etc.

[0201] Reference Figure 7 The electronic device 700 may include one or more of the following components: a processing component 702, a memory 704, a power supply component 706, a multimedia component 708, an audio component 710, an input / output (I / O) interface 712, a sensor component 714, and a communication component 716.

[0202] Processing component 702 typically controls the overall operation of electronic device 700, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 702 may include one or more processors 720 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 702 may include one or more modules to facilitate interaction between processing component 702 and other components. For example, processing component 702 may include a multimedia module to facilitate interaction between multimedia component 708 and processing component 702.

[0203] Memory 704 is configured to store various types of data to support the operation of device 700. Examples of this data include instructions for any application or method operating on electronic device 700, contact data, phonebook data, messages, pictures, videos, etc. Memory 704 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0204] Power supply component 706 provides power to various components of electronic device 700. Power supply component 706 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 700.

[0205] Multimedia component 708 includes a screen that provides an output interface between the aforementioned electronic device 700 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 708 includes a front-facing camera and / or a rear-facing camera. When the electronic device 700 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0206] Audio component 710 is configured to output and / or input audio signals. For example, audio component 710 includes a microphone (MIC) configured to receive external audio signals when electronic device 700 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 704 or transmitted via communication component 716. In some embodiments, audio component 710 also includes a speaker for outputting audio signals.

[0207] I / O interface 712 provides an interface between processing component 702 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0208] Sensor assembly 714 includes one or more sensors for providing state assessments of various aspects of electronic device 700. For example, sensor assembly 714 can detect the on / off state of electronic device 700, the relative positioning of components such as the display and keypad of electronic device 700, changes in position of electronic device 700 or one of its components, the presence or absence of user contact with electronic device 700, orientation or acceleration / deceleration of electronic device 700, and temperature changes of electronic device 700. Sensor assembly 714 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 714 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 714 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0209] Communication component 716 is configured to facilitate wired or wireless communication between electronic device 700 and other devices. Electronic device 700 can access wireless networks based on communication standards, such as WiFi, 4G or 5G, 4G LTE, 5G NR, or combinations thereof. In one exemplary embodiment, communication component 716 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the aforementioned communication component 716 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0210] In an exemplary embodiment, the electronic device 700 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0211] In an exemplary embodiment, a non-transitory computer-readable storage medium, such as a memory 704 including instructions, is also provided. A selection method is provided to enable the electronic device 700 to perform an interactive action when the instructions in the storage medium are executed by the processor 720 of the electronic device 700. The method includes:

[0212] Obtain the pixels in the image to be processed that need noise reduction;

[0213] Based on the pixel to be denoised, multiple pixel bands in the image to be processed are obtained. Each pixel band includes multiple consecutive pixels centered on the pixel to be denoised. Each pixel band includes the same number of pixels, and the multiple pixel bands are located in multiple directions centered on the pixel to be denoised.

[0214] For each pixel band in each direction, obtain the correction parameters of each associated pixel in the pixel band for the pixel to be denoised. The associated pixel is the pixel in the pixel band other than the pixel to be denoised. The correction parameters are used to characterize the noise included in the pixel to be denoised.

[0215] Based on the correction parameters of each associated pixel in the pixel band in each direction, the pixel value of the pixel to be denoised is adjusted to obtain the denoised pixel value.

[0216] The non-transitory computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0217] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0218] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. An image denoising method, characterized in that, The method includes: Obtain the pixel to be denoised in the image to be processed; based on the pixel to be denoised, obtain multiple pixel bands in the image to be processed, each pixel band including multiple consecutive pixels centered on the pixel to be denoised, each pixel band including the same number of pixels, and the multiple pixel bands are respectively located in multiple directions centered on the pixel to be denoised. For each pixel band in each direction, obtain the correction parameters of each associated pixel in the pixel band for the pixel to be denoised. The associated pixel is the pixel in the pixel band other than the pixel to be denoised. The correction parameters are used to characterize the noise included in the pixel to be denoised. Based on the correction parameters of each associated pixel in the pixel band in each direction, the pixel value of the pixel to be denoised is adjusted to obtain the denoised pixel value. For each pixel band in each direction, the step of obtaining the correction parameters of each associated pixel in the pixel band for the pixel to be denoised includes: determining the peripheral noise-to-signal ratio of the pixel to be denoised based on the pixel value of each pixel in the multiple pixel bands; and determining the denoising strength parameter of the pixel to be denoised based on the peripheral noise-to-signal ratio of the pixel to be denoised and the brightness information of the pixel to be denoised. For any associated pixel in the pixel band, a noise threshold is determined based on the denoising intensity parameter of the pixel to be denoised, the pixel value of the pixel to be denoised, and the pixel value of the associated pixel. If the noise threshold is greater than the pixel difference, the pixel difference is used as the estimated noise corresponding to the associated pixel, where the pixel difference is the difference between the pixel value of the associated pixel and the pixel value of the pixel to be denoised. Alternatively, if the noise threshold is less than the pixel difference, the noise threshold is used as the estimated noise corresponding to the associated pixel. Based on the estimated noise corresponding to each associated pixel in the pixel band and the weight parameter corresponding to each associated pixel, a correction parameter for the pixel to be denoised by the associated pixel is determined.

2. The method according to claim 1, characterized in that, The step of determining the noise threshold based on the denoising intensity parameter of the pixel to be denoised, the pixel value of the pixel to be denoised, and the pixel value of the associated pixel includes: Based on the pixel values ​​of the pixel to be denoised and the pixel values ​​of the associated pixel, the noise threshold parameter is obtained; Based on the denoising intensity parameter of the pixel to be denoised and the noise threshold parameter, the noise threshold is determined; wherein, the denoising intensity parameter of the pixel to be denoised satisfies the following condition: the greater the brightness of the pixel to be denoised, the smaller the denoising intensity parameter of the pixel to be denoised.

3. The method according to claim 1, characterized in that, Determining the peripheral noise-to-signal ratio of the pixel to be denoised based on the pixel values ​​of each pixel in the plurality of pixel bands includes: For each pixel band in a direction, determine the average pixel value of each pixel in that pixel band. The pixel change parameter corresponding to the pixel band in that direction is determined based on the difference between the average pixel value of each pixel in the pixel band in that direction and the pixel value of each pixel. The peripheral signal-to-noise ratio of the pixel to be denoised is determined based on the pixel change parameters corresponding to the pixel bands in each direction.

4. The method according to claim 1, characterized in that, The step of adjusting the pixel value of the pixel to be denoised based on the correction parameters of each associated pixel in the pixel band in each direction to obtain the denoised pixel value includes: For each pixel band in a direction, the pixel change parameter corresponding to that pixel band is determined based on the pixel value of each pixel in that pixel band. The signal strength of the pixel to be denoised is determined based on the pixel change parameters corresponding to the pixel bands in each direction. Based on the signal strength of the pixel to be denoised and the pixel change degree parameter corresponding to the pixel band in that direction, the normalization parameter corresponding to the pixel band in that direction is obtained; wherein, the normalization parameter satisfies the following: when the pixel change degree parameter is equal to the signal strength, the normalization parameter is 0; Based on the correction parameters of each associated pixel in the pixel band in each direction for the pixel to be denoised, and the normalization parameters corresponding to the pixel band in the corresponding direction, the pixel value of the pixel to be denoised is adjusted to obtain the denoised pixel value; wherein, when the normalization parameter is 0, the correction parameters of each associated pixel in the pixel band in the corresponding direction for the pixel to be denoised do not participate in the adjustment of the pixel value of the pixel to be denoised.

5. An image noise reduction device, characterized in that, The device includes: The module for acquiring pixels to be denoised is used to acquire the pixels to be denoised in the image to be processed; The pixel band acquisition module is used to acquire multiple pixel bands in the image to be processed based on the pixel to be denoised. Each pixel band includes multiple consecutive pixels centered on the pixel to be denoised. The number of pixels included in each pixel band is the same, and the multiple pixel bands are respectively located in multiple directions centered on the pixel to be denoised. The correction parameter acquisition module is used to acquire, for each direction of the pixel band, the correction parameters of each associated pixel point in the pixel band to the pixel point to be denoised, wherein the associated pixel points are the pixel points in the pixel band other than the pixel point to be denoised, and the correction parameters are used to characterize the noise included in the pixel point to be denoised. The pixel value adjustment module is used to adjust the pixel value of the pixel to be denoised based on the correction parameters of each associated pixel in the pixel band in each direction, so as to obtain the denoised pixel value. The correction parameter acquisition module, when acquiring the correction parameters of each associated pixel in the pixel band for the pixel to be denoised for each direction, includes: Based on the pixel values ​​of each pixel in the plurality of pixel bands, the peripheral noise-to-signal ratio of the pixel to be denoised is determined; The noise reduction intensity parameter of the pixel to be denoised is determined based on the peripheral noise-to-signal ratio of the pixel to be denoised and the brightness information of the pixel to be denoised. For any associated pixel in the pixel band, the estimated noise corresponding to the associated pixel is obtained based on the pixel value of the pixel to be denoised, the denoising strength parameter of the pixel to be denoised, and the pixel value of the associated pixel. Based on the estimated noise corresponding to each associated pixel in the pixel band and the weight parameters corresponding to each associated pixel, the correction parameters of the associated pixel for the pixel to be denoised are determined. The correction parameter acquisition module, when used to acquire the estimated noise corresponding to any associated pixel point on the pixel band based on the pixel value of the pixel to be denoised, the denoising intensity parameter of the pixel to be denoised, and the pixel value of the associated pixel point, includes: The noise threshold is determined based on the noise reduction intensity parameters of the pixel to be denoised, the pixel value of the pixel to be denoised, and the pixel value of the associated pixel. In response to the noise threshold being greater than the pixel difference, the pixel difference is used as the estimated noise, where the pixel difference is the difference between the pixel value of the associated pixel and the pixel value of the pixel to be denoised; Alternatively, in response to the noise threshold being less than the pixel difference, the noise threshold is used as the estimated noise.

6. The apparatus according to claim 5, characterized in that, The correction parameter acquisition module, when determining the noise threshold based on the denoising intensity parameter of the pixel to be denoised, the pixel value of the pixel to be denoised, and the pixel value of the associated pixel, includes: Based on the pixel values ​​of the pixel to be denoised and the pixel values ​​of the associated pixel, the noise threshold parameter is obtained; Based on the denoising intensity parameter of the pixel to be denoised and the noise threshold parameter, the noise threshold is determined; wherein, the denoising intensity parameter of the pixel to be denoised satisfies the following condition: the greater the brightness of the pixel to be denoised, the smaller the denoising intensity parameter of the pixel to be denoised.

7. The apparatus according to claim 5, characterized in that, The correction parameter acquisition module, when determining the peripheral noise-to-signal ratio of the pixel to be denoised based on the pixel values ​​of each pixel in the plurality of pixel bands, includes: For each pixel band in a direction, determine the average pixel value of each pixel in that pixel band. The pixel change parameter corresponding to the pixel band in that direction is determined based on the difference between the average pixel value of each pixel in the pixel band in that direction and the pixel value of each pixel. The peripheral signal-to-noise ratio of the pixel to be denoised is determined based on the pixel change parameters corresponding to the pixel bands in each direction.

8. The apparatus according to claim 5, characterized in that, The pixel value adjustment module, when adjusting the pixel value of the pixel to be denoised based on the correction parameters of each associated pixel in the pixel band in each direction to obtain the denoised pixel value, includes: For each pixel band in a direction, the pixel change parameter corresponding to that pixel band is determined based on the pixel value of each pixel in that pixel band. The signal strength of the pixel to be denoised is determined based on the pixel change parameters corresponding to the pixel bands in each direction. Based on the signal strength of the pixel to be denoised and the pixel change degree parameter corresponding to the pixel band in that direction, the normalization parameter corresponding to the pixel band in that direction is obtained; wherein, the normalization parameter satisfies the following: when the pixel change degree parameter is equal to the signal strength, the normalization parameter is 0; Based on the correction parameters of each associated pixel in the pixel band in each direction for the pixel to be denoised, and the normalization parameters corresponding to the pixel band in the corresponding direction, the pixel value of the pixel to be denoised is adjusted to obtain the denoised pixel value; wherein, when the normalization parameter is 0, the correction parameters of each associated pixel in the pixel band in the corresponding direction for the pixel to be denoised do not participate in the adjustment of the pixel value of the pixel to be denoised.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method described in any one of claims 1 to 4.

10. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to perform the image denoising method according to any one of claims 1 to 4.

Images